Beyond Facts – Knowledge Graphs for Online Discourse (KnOD) 2020

Collocated with the Extended Semantic Web Conference 2020, May 31 – June 4, Heraklion (Crete, Greece)

Expressing opinions and interacting with others on the Web has led to the production of an abundance of online discourse data, such as claims and viewpoints on controversial topics, their sources and contexts (events, entities). This data constitutes a valuable source of insights for studies into misinformation spread, bias reinforcement, echo chambers or political agenda setting.  While knowledge graphs (KGs) promise to provide the key to a Web of structured information, they are mainly focused on facts without keeping track of the diversity, connection or temporal evolution of online discourse data. As opposed to facts, claims are inherently more complex. Their interpretation strongly depends on the context and a variety of intentional or unintended meanings, where terminology and conceptual understandings strongly diverge across communities from computational social science, to argumentation mining, fact-checking, or viewpoint/stance detection.

This workshop aims at strengthening the relations between the aforementioned communities providing a forum for shared works on the modeling, extraction and analysis of discourse on the Web. It will address the need for a shared understanding and structured knowledge about discourse data in order to enable machine-interpretation, discoverability and reuse, in support of scientific or journalistic studies into the analysis of societal debates on the Web.

Beyond Semantic Web research into information extraction, data consolidation and modeling for KG building, the workshop targets communities focusing on the analysis of online discourse, relying on methods from machine learning (ML), natural language processing (NLP) and data mining (DM). These include communities on: 

  • discourse analysis
  • argumentation mining 
  • computational fact-checking / truth discovery 
  • mis- and dis-information spread 
  • bias and controversy detection and analysis
  • stance / viewpoint detection and representation and opinion mining
  • rumour, propaganda and hate-speech detection

KnOD provides a meeting point for these related but distinct communities that address similar or closely related questions from different perspectives and in different fields, using different models and definitions of the main notions of interest. Often these communities apply their research in particular domains, such as scientific publishing, medicine, journalism or social science. Therefore, the workshop is particularly interested in works that apply an interdisciplinary approach, such as works on computational social sciences or computational journalism.

Topics of interest include, but are not limited to, the following: 

  • Ontologies and data models for claims, stances/viewpoints, topics, sources and other online discourse data
  • Reuse and extension of existing models such as schema.org and Wikidata
  • KGs and knowledge extraction techniques 
  • Computational fact-checking / truth discovery
  • Bias and controversy detection and analysis
  • Stance and viewpoint discovery
  • Rumour, propaganda and hate-speech detection
  • Integration, aggregation, linking and enrichment of discourse data
  • Semantic and exploratory search 
  • Argumentation and reasoning over discourse data
  • Recommender systems for discourse data
  • Quality, uncertainty, provenance, and trust of discourse data
  • Benchmarks and training data for extraction, verification or linking of discourse data
  • Use-cases, applications and cross-community interfaces